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from langchain_community.vectorstores import Neo4jVector
from langchain_openai import OpenAIEmbeddings
# Typical RAG retriever
typical_rag = Neo4jVector.from_existing_index(
OpenAIEmbeddings(), index_name="typical_rag"
)
# Parent retriever
parent_query = """
MATCH (node)<-[:HAS_CHILD]-(parent)
WITH parent, max(score) AS score // deduplicate parents
RETURN parent.text AS text, score, {} AS metadata LIMIT 1
"""
parent_vectorstore = Neo4jVector.from_existing_index(
OpenAIEmbeddings(),
index_name="parent_document",
retrieval_query=parent_query,
)
# Hypothetic questions retriever
hypothetic_question_query = """
MATCH (node)<-[:HAS_QUESTION]-(parent)
WITH parent, max(score) AS score // deduplicate parents
RETURN parent.text AS text, score, {} AS metadata
"""
hypothetic_question_vectorstore = Neo4jVector.from_existing_index(
OpenAIEmbeddings(),
index_name="hypothetical_questions",
retrieval_query=hypothetic_question_query,
)
# Summary retriever
summary_query = """
MATCH (node)<-[:HAS_SUMMARY]-(parent)
WITH parent, max(score) AS score // deduplicate parents
RETURN parent.text AS text, score, {} AS metadata
"""
summary_vectorstore = Neo4jVector.from_existing_index(
OpenAIEmbeddings(),
index_name="summary",
retrieval_query=summary_query,
)
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